Coronary artery disease is a leading global cause of mortality. It can be diagnosed by fractional flow reserve (FFR) estimation using computed tomography (CT) angiography. This systematic review aims to review the literature about the diagnostic efficacy of FFR estimation using CT scan (FFR-CT) for the diagnosis of coronary artery disease. The dual databases of Medline and Cochrane Central Register of Controlled Trials were searched for relevant literature from their inception till August 15, 2017. The methodological quality was assessed using the Cochrane risk of bias tool. Pooled estimates of specificity and sensitivity were assessed with the corresponding 95% confidence intervals (CI). After careful screening, five studies involving a total of 296 patients were included in the study. For FFR-CT, on meta-analysis of the pooled risk ratio per patient, random-effects model value was 3.79 (95% CI, 2.93–4.90) and odds ratio per patient was 11.78 (95% CI, 8.08–17.17). The odds ratio by year to see if heterogeneity is due to sample size was 2.50 (95% CI, 1.06–5.91). FFR-CT appears to be a reliable and efficacious noninvasive imaging modality, as it demonstrates high accuracy in the determination of anatomy and lesion-specific ischemia, which justifies the performance of additional randomized controlled trials to evaluate the clinical benefits of FFR-CT-guided coronary revascularization.

Coronary artery disease is among the leading causes of morbidity globally.[1] Nearly, 40% of the patients exposed to angiography have nonsignificant coronary artery disease.[2] The fractional flow reserve (FFR) measurement is the current standard for the functional assessment of lesion severity to diagnose coronary artery disease. Computation of FFR from computed tomography (CT) angiography (FFR-CT) gives a noninvasive method to identify coronary artery disease causing stenosis. FFR calculation during invasive angiography is another modality which is considered as the current gold standard. An accurate FFR estimation not only increases event-free survival and decreases unnecessary revascularization, but also significantly reduces the overall health-care expenditure.[3],[4] Till date, there are conflicting data regarding the diagnostic efficacy of FFR-CT angiography for the diagnosis of coronary artery disease. Some of the earlier studies were composed of single-center studies or limited multicentric studies. Moreover, there are no studies which have systematically evaluated the technique using the latest data available till August 2016. Thus, we performed a systemic review and meta-analysis to answer the important question.

Methods and Analysis

The study was performed using the standard guidelines of the quality of meta-analyses reporting of Preferred Reporting Items for Systematic reviews and Meta-Analyses statement.[5]

Search strategy

The primary computerized search was conducted by cross-searching Medline and Cochrane Central Register of Controlled Trials databases, using the terms “computed tomography” or “CT” and “fractional flow reserve”

or “FFR.” All English publications were included with human participants of all ages till August 14, 2016, without any further restrictions. Nonoriginal research studies (editorials and narrative reviews) and meeting abstracts were excluded out due to limited available information. The references of retrieved reports and pertinent reviews were also examined for potentially overlapping data and additional reports.

Study selection

Data extraction and quality assessment were conducted by two reviewers after collection of the data independently, with due consideration to the eligibility and exclusion criteria. Agreement between reviewers was calculated using Kappa statistics (kappa index 1 / 4 0.924, indicating excellent agreement between the two reviewers). The study selection pattern is described in the flowchart in [Figure 1]. Disagreements were mediated by a third reviewer to reach a consensus. Inclusion criteria used for entry into meta-analysis are as follows: (1) diagnostic accuracy design of study; (2) adult (18 years and older) study participants with suspected coronary artery disease; (3) the index test was FFR using invasive angiography or CT; and (4) possibility of construction of two-by-two contingency table. The extracted data were divided into the following specific data extraction forms: (1) study characteristics: first author, publication date, study populations, study design, number of patients; (2) patient characteristics: proportion of men, mean age, body mass index, mean heart rate, proportion of medical history (hypertension, diabetes mellitus, dyslipidemia, smoking history, prior revascularization, and myocardial infarction), left ventricular ejection fraction, proportion of patients and vessels with FFR 0.80, and creatinine level; and (3) FFR-CT parameters: time period between FFR-CT and sign onset, type of CT system used, administration of drugs as beta-blockers, preexamination administration of nitrates, tube voltage, tube current, reconstruction methods, contrast agent administration, radiation dose, and (4) data to create 2 × 2 tables for the concordance of FFR-CT with FFR: true positives, false positives, false negatives, and true negatives, or sensitivity and specificity. As shown in [Table 1] and [Table 2], two independent investigators evaluated the quality of the included studies using a tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS).[6] As described by Whiting et al., this useful tool consists of a list of 14 questions that should be answered as “yes,” “no,” or “unclear.”[7]

Sensitivity and specificity data were calculated as the major outcome measures. As shown in Excelsheet attachment under all data sections, the pooled sensitivity, specificity, positive predictive value, negative predictive value (NPV), positive likelihood ratio (LR+), and negative LR (LR−) with the corresponding 95% confidence interval (CI) were calculated. Data were pooled using weighted averages according to the sample size of each study on a per-patient and per-vessel or per-lesion basis using either random-effects or fixed-effects approaches, depending on the presence of statistical heterogeneity.[8] Statistical heterogeneity was defined as an I2 statistic value of more than 50%.[9] Potential heterogeneity, meaning variation between studies, was evaluated by calculating the H statistic and I2 statistic. Significant heterogeneity was defined as H statistic >1.5 and or I2 > 50%. A random-effects model namely Der Simoniane Laird model was used for data with heterogeneity and a fixed-effects model namely Mantel Haenszel model was used for data without any heterogeneity. To identify the sources of heterogeneity, a meta-regression analysis was also performed. Predefined sources of heterogeneity included study design, proportion of diabetic patients, number of patients, percentage of coronary artery lumen stenosis ≥50% (FFR-CT), and funding support. The meta-analysis was performed using RevMan 5.3 (Review Manager 2012: The Cochrane Collaboration, The Nordic Cochrane Centre, Copenhagen, Denmark) and STATA 13.0.

Assessment of risk of bias in included studies

To rule out publication bias, effective sample size funnel plots were constructed by combining the sensitivity and specificity of each study into diagnostic odds ratios (DORs), and then plotting the natural logarithm of the DOR (LnOR) against the standard error of LnOR. Funnel plot asymmetry was analyzed to rule out deficient reports of negative study outcomes as shown in [Figure 2]. Further subgroup analysis was performed using an additional analysis to calculate which of the FFR type is associated with better outcomes post-nipple-sparing mastectomy (NSM).[10] “The Cochrane Risk of Bias Tool for RCTs was used to examine the domains of the blinding of participants, namely sequence generation; personnel and outcome assessors; allocation concealment; incomplete outcome data; selective outcome reporting; and other sources.[11] However, the relevant Cochrane Risk of Bias Assessment Tool ACROBAT-NRSI was used for nonrandomized studies.”[12]

When possible, NSM and secondary structure matching will be compared. Using Review Manager V.5.2.6 (RevMan), an assessment of heterogeneity in comparative studies will be made. In case of high I2 statistic (I2 > 50%), meta-analysis will be performed using a random-effects model, otherwise a fixed-effects model will be used.[13]

Results

A total of five studies that enrolled 296 patients were included in this current review. Since I2 statistic was more than 0.70, random-effects model was used for meta-analysis. Subsequent meta-analysis of the pooled risk ratio per patient was 3.79 (95% CI, 2.93–4.90) and odds ratio per patient was 11.78 (95% CI, 8.08–17.17).

As shown in [Table 1], all the selected five studies had overall high-quality scores for a total of 14 items of the QUADAS questionnaire. The odds ratio by year to see if heterogeneity is due to sample size was 2.50 (95% CI, 1.06–5.91). The per-patient detailed pooled odds ratio 11.781 (95% CI, 8.083–17.171), risk ratio 3.790 (95% CI, 2.929–4.906), and their forest plots to reveal heterogeneity are shown in [Figure 2], [Figure 3], [Figure 4]. As shown in [Figure 5] and [Figure 6] the I2 (variation in risk ratio attributable to heterogeneity) was 84 and the I2 (variation in odds ratio attributable to heterogeneity) was 80. The funnel plot with pseudo 95% confidence limits resembled a symmetrical funnel shape. Moreover, considering the limited number of articles selected (less than 10), we did not opt for further meta-regression analysis after the initial funnel plot.

The FFR is defined as the ratio of maximal flow of blood through diseased coronary vessel to a hypothetical normal vessel.[14],[15],[16],[17] An FFR with value of 1.0 is accepted as normal, whereas a value lower than 0.75–0.80 is considered to be as myocardial ischemic. FFR-CT has proved clinically useful to rule out obstructive coronary artery disease due to the high sensitivity and NPV associated with its technology. The main challenge for FFR-CT remains is its ability to identify functionally significant coronary artery disease.[18] The present meta-analysis provides the latest and updated compilation of available clinical data on the diagnostic performance of FFR-CT for the evaluation of myocardial ischemia in patients with suspected or known coronary artery disease, with invasive FFR-A as the reference standard. To determine the next step, interventions such as revascularization FFR estimation and its accuracy command significant attention. Earlier, Fryback and Thornbury devised a popular model showing several hierarchical levels of evidence of diagnostic test. “The 6 tiers of evidence included parameters such as technical efficacy, diagnostic thinking, diagnostic accuracy, patient outcomes, therapeutic efficacy, and the last but not the least societal efficacy including cost-effectiveness.”[19] However, the total number of recommendations for cardiovascular imaging in American Heart Association and American College of Cardiology guidelines on the basis of Level of Evidence A can be counted on our fingers.[20] This present study aims to throw some light on the gray areas for making of clinical decision.

The following five studies were finally selected as shown in [Table 1].

DISCOVERY-FLOW trial was a prospective, multicentric, and blinded trial to compare the efficacy of FFR-CT directly with invasive angiography. It did not have any allocation concealment. Although there were no sponsors, testing was performed by Heartflow™ team members

DeFacto clinical trial was a prospective, multicentric clinical trial without any allocation concealment. This clinical trial received funding from Heartflow™. Interestingly, both trials, namely DISCOVER-FLOW and DeFACTO, in spite of having identical study designs, did not achieve primary prospective endpoint of diagnostic accuracy of FFR-CT compared with invasive angiography. Detailed statistical analysis revealed the reason to be insufficient number of enrolled patients

Next steps trial published in 2014 was a prospective, single–blinded, multicentric clinical trial without allocation concealment. It used a refined version of FFR-CT model and showed better results compared to DISCOVERY-FLOW and DeFacto. This clinical trial had received active funding from Heartflow™

The study by Renker et al. was a single-center-based, single-blinded, and retrospective study without any allocation concealment enrolling 53 patients with 67 lesions with funding from Heartflow™

The clinical trial by Thompson et al. is the latest prospective, single-blinded, and multicentric clinical trial actively funded by Heartflow™. This is the single-most study showing significant rise in the diagnostic accuracy of CT angiography-mediated FFR. The clinical application of FFR estimation using noninvasive CT and subsequent proper treatment planning approach can reduce clinically unnecessary interventions and overall costs of patient and the health-care ecosystem.

Conclusions

FFR estimation using CT angiography is a reliable and efficacious noninvasive imaging modality, as it demonstrates high accuracy in the determination of anatomy and lesion-specific ischemia, which justifies the performance of additional randomized controlled trials to evaluate the clinical benefits of FFR-CT-guided coronary revascularization.

Strengths and limitations of this study

The strengths include the noncommercial meta-analysis and systemic review was conducted by a multidisciplinary team

This team has specific training and experience in conducting systemic review and meta-analyses

A limitation of this study is that only a small number of patients were included in the study

A potential weakness is reporting bias in the included studies.

Ethics and dissemination

This systematic review required no ethical approval. This review for peer-review journal was conducted to guide health-care practices and policies of clinicians and researchers.

Whiting P, Rutjes AW, Reitsma JB, Bossuyt PM, Kleijnen J. The development of QUADAS: A tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC medical research methodology 2003;3:25.